LLMs are useful: they summarize quickly, generate drafts, and help you think. But there’s a failure mode that makes them risky as a “source of truth”: hallucinations.
A hallucination is a plausible-sounding answer that is not grounded in verifiable facts or the right context. Often it comes with a confident tone.
This article explains why that happens and what to do so your answers become checkable.
Why LLMs make confident mistakes
Usually it’s a combination of factors:
1) The model predicts text, not truth
By default, an LLM doesn’t go out into the world to fetch facts. It produces likely text based on training patterns and your prompt.
2) Missing context gets filled with “typical”
If the question is abstract, the model fills gaps with what “usually” happens. In real workflows, the gaps contain the important nuance.
3) Knowledge gets outdated
Even if an answer was correct once, things change: policies, pricing, versions, best practices.
4) Multiple approaches get blended into one
The model can merge different (sometimes conflicting) patterns into a single neat answer that has never worked end-to-end.
5) Tone hides uncertainty
Humans read confidence as competence. A confident mistake can be worse than “I don’t know.”
What to do: five practical principles for reliable answers
Principle 1) Require sources and primary evidence
If you can’t verify it, treat it as a hypothesis.
A reliable format in real operations looks like:
- the answer
- links to sources
- constraints / applicability conditions
Principle 2) Anchor the answer in your context
Most advice depends on:
- who it’s for (newcomers vs experts)
- where it runs (support chat vs expert community vs internal team)
- constraints (time, budget, permissions, policy)
A good question is half of a reliable answer.
Principle 3) Separate explanation from instructions
Ask for:
- why this is recommended
- step-by-step instructions
- risks and exceptions
When the model is forced to name risks, many hallucinations surface.
Principle 4) Validate with examples and test cases
Reliable answers can be tested. Ask for:
- 2-3 examples where it applies
- 2 examples where it doesn’t
- a minimal test to know it works
Principle 5) Use expert communities for freshness
In 2026, the most reliable path for practical questions is often not “the perfect article” but people who have done it recently.
Expert chats provide:
- fresh cases
- follow-up questions and clarifications
- multiple viewpoints
- real constraints
But chats have a weakness: knowledge sinks in the stream. That means you need a knowledge layer over chat history.
Why “answers with sources” beat “smart answers”
When an answer includes a link to the source (a message, thread, or doc), you get:
- verification
- context (why this was decided)
- a path to ask the author follow-ups
- faster onboarding (new members learn how the community thinks)
This is also community management: trust rises and pointless debates decrease.
How AskMore reduces hallucination risk
AskMore is built around “meaning-based search + source links to chat messages.” This helps you:
- find relevant past discussions even with different phrasing
- see quotes/source messages instead of context-free guesses
- summarize long threads into a clear takeaway
Comparison: AskMore vs Telegram Search: When You Need a Bot.
Background: Semantic Search Explained (In Plain Words).
A mini checklist for reliable answers
When you get an answer from an LLM (or anywhere), check:
- Are there sources/primary references?
- Are applicability conditions clear (where it works, where it doesn’t)?
- Are there examples and a test case?
- Could it be outdated?
- Can I confirm with a real human if it’s critical?
If you run a community, it helps to encode these principles in onboarding and rules so “verifiable answers” becomes the norm.
Try AskMore on Telegram: https://t.me/AskMoreBot